Bearing Weak Fault Feature Extraction Under Time-Varying Speed Conditions Based on Frequency Matching Demodulation Transform

Bearing weak fault feature extraction under time-varying speed conditions is a challenging task. The classic time-frequency analysis (TFA) based ridge detection algorithms cannot work well for detecting time-varying fault characteristic frequency (FCF) harmonics of the bearing due to the limitation of time-frequency resolution and noise interference. As such, a novel frequency matching demodulation transform (FMDT) technique is developed by extending the generalized demodulation transform for bearing weak fault feature extraction and diagnosis under variable speeds. The main novelties of this article are concluded that: the FMDT can first estimate FCF under strong noise conditions by generating a series of demodulation parameters to match FCF with the criterion that the maximal peak can be captured at a specific frequency from the spectra of the demodulated signals, and then calculate rotational frequency (RF) harmonics; and the proposed FMDT-based method does not require the measured bearing RF, and can directly determine bearing fault type using the estimated FCF and RF harmonics. The effectiveness of the FMDT-based bearing fault diagnosis method is verified by both simulated and experimental results. Comparison analysis shows that the proposed method can generate much better results than the classic TFA-based ridge detection algorithms.